{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick example to fit a baseline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import rampy\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0520258250>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_points  =500\n",
    "x = np.linspace(50, 500, nb_points)\n",
    "\n",
    "# gaussian peaks\n",
    "p1 = 20.0 * np.exp(-np.log(2) * ((x-150.0)/15.0)**2)\n",
    "p2 = 100.0 * np.exp(-np.log(2) * ((x-250.0)/5.0)**2)\n",
    "p3 = 50.0 * np.exp(-np.log(2) * ((x-450.0)/1.0)**2)\n",
    "p4 = 20.0 * np.exp(-np.log(2) * ((x-350.0)/30.0)**2)\n",
    "p5 = 30.0 * np.exp(-np.log(2) * ((x-460.0)/5.0)**2)\n",
    "\n",
    "# background: a large gaussian + linear \n",
    "bkg = 60.0 * np.exp(-np.log(2) * ((x-250.0)/200.0)**2) + 0.1*x\n",
    "\n",
    "#noise\n",
    "noise = 2.0 * np.random.normal(size=nb_points)\n",
    "\n",
    "#observation\n",
    "y = p1 + p2 + p3 + p4 + p5 + noise +bkg\n",
    "\n",
    "plt.plot(x,y,\"r-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now using different baselines to retrieve the true background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f051c992110>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# need to define some fitting regions for the spline\n",
    "roi = np.array([[0,100],[200,220],[280, 290],[420,430],[480,500]])\n",
    "\n",
    "# calculating the baselines\n",
    "ycalc_poly, base_poly = rampy.baseline(x,y,roi,'poly',polynomial_order=3 )\n",
    "#ycalc_gcvspl, base_gcvspl = rampy.baseline(x,y,roi,'gcvspline',s=0.1 ) # activate if you have installed gcvspline\n",
    "ycalc_uni, base_uni = rampy.baseline(x,y,roi,'unispline',s=1e0)\n",
    "ycalc_als, base_als = rampy.baseline(x,y,roi,'als',lam=10**7,p=0.05)\n",
    "ycalc_arpls, base_arpsl = rampy.baseline(x,y,roi,'arPLS',lam=10**7,ratio=0.001)\n",
    "ycalc_drpls, base_drpsl = rampy.baseline(x,y,roi,'drPLS')\n",
    "\n",
    "# doing the figure\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(x,y,\"k-\",label=\"Raw\")\n",
    "plt.plot(x,bkg,\"r-\",label=\"Bkg\")\n",
    "\n",
    "plt.plot(x,base_poly,\"-\",color=\"grey\",label=\"polynomial\")\n",
    "plt.plot(x,base_uni,\"b-\",label=\"unispline baseline\")\n",
    "#plot(x,base_gcvspl,\"-\",color=\"orange\",label=\"gcvspline baseline\") # activate if you have installed gcvspline\n",
    "plt.plot(x,base_als,\":\",color=\"purple\",label=\"als baseline\")\n",
    "plt.plot(x,base_arpsl,\"-.\",color=\"cyan\",label=\"arPLS baseline\")\n",
    "plt.plot(x,base_drpsl,\"--\",color=\"cyan\",label=\"drPLS baseline\")\n",
    "\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function baseline in module rampy.baseline:\n",
      "\n",
      "baseline(x_input, y_input, bir, method, **kwargs)\n",
      "    Allows subtracting a baseline under a x y spectrum.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x_input : ndarray\n",
      "        x values.\n",
      "    y_input : ndarray\n",
      "        y values.\n",
      "    bir : ndarray\n",
      "        Contain the regions of interest, organised per line.\n",
      "        For instance, roi = np.array([[100., 200.],[500.,600.]]) will\n",
      "        define roi between 100 and 200 as well as between 500 and 600.\n",
      "        Note: This is NOT used by the \"als\" and \"arPLS\" algorithms, but still is a requirement when calling the function.\n",
      "        bir and method probably will become args in a futur iteration of rampy to solve this.\n",
      "    method : str\n",
      "        \"poly\": polynomial fitting, with splinesmooth the degree of the polynomial.\n",
      "        \"unispline\": spline with the UnivariateSpline function of Scipy, splinesmooth is\n",
      "                     the spline smoothing factor (assume equal weight in the present case);\n",
      "        \"gcvspline\": spline with the gcvspl.f algorythm, really robust.\n",
      "                     Spectra must have x, y, ese in it, and splinesmooth is the smoothing factor;\n",
      "                     For gcvspline, if ese are not provided we assume ese = sqrt(y).\n",
      "                     Requires the installation of gcvspline with a \"pip install gcvspline\" call prior to use;\n",
      "        \"exp\": exponential background;\n",
      "        \"log\": logarythmic background;\n",
      "        \"rubberband\": rubberband baseline fitting;\n",
      "        \"als\": (automatic) baseline least square fitting following Eilers and Boelens 2005;\n",
      "        \"arPLS\": (automatic) Baseline correction using asymmetrically reweighted penalized least squares smoothing. Baek et al. 2015, Analyst 140: 250-257;\n",
      "        'drPLS': (automatic) Baseline correction method based on doubly reweighted penalized least squares. Xu et al., Applied Optics 58(14):3913-3920.\n",
      "    \n",
      "    kwargs\n",
      "    ------\n",
      "    polynomial_order : Int\n",
      "        The degree of the polynomial (0 for a constant), default = 1.\n",
      "    s : Float\n",
      "        spline smoothing coefficient for the unispline and gcvspline algorithms.\n",
      "    lam : Float\n",
      "        float, the lambda smoothness parameter for the ALS, ArPLS and drPLS algorithms. Typical values are between 10**2 to 10**9, default = 10**5 for ALS and ArPLS and default = 10**6 for drPLS.\n",
      "    p : Float\n",
      "        float, for the ALS algorithm, advised value between 0.001 to 0.1, default = 0.01.\n",
      "    ratio : float\n",
      "        ratio parameter of the arPLS and drPLS algorithm. default = 0.01 for arPLS and 0.001 for drPLS.\n",
      "    niter : Int\n",
      "        number of iteration of the ALS and drPLS algorithm, default = 10 for ALS and default = 100 for drPLS.\n",
      "    eta : Float\n",
      "        roughness parameter for the drPLS algorithm, is between 0 and 1, default = 0.5\n",
      "    p0_exp : List\n",
      "        containg the starting parameter for the exp baseline fit with curve_fit. Default = [1.,1.,1.].\n",
      "    p0_log : List\n",
      "        containg the starting parameter for the log baseline fit with curve_fit. Default = [1.,1.,1.,1.].\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out1 : ndarray\n",
      "        Contain the corrected signal.\n",
      "    out2 : ndarray\n",
      "        Contain the baseline.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(rampy.baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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